AI-powered drawing extraction modernized a US university’s powerplant archive, digitizing 70+ years of records with OCR, indexing, and feature extraction for fast, accurate, and searchable access.
A leading university struggled to manage seven decades of power plant documentation, ranging from the 1930s to the 2000s. Historical engineering drawings and manuals lacked indexing, making them nearly impossible to search or reference. The files were handwritten and hand drawn, that had deteriorated over time, with faded text, noise, and unclear diagrams. The large size of engineering drawings made storage and processing difficult, while manual cataloging of thousands of documents was slow and error-prone. Critical technical details remained inaccessible in unstructured, unsearchable formats, creating major bottlenecks for research, maintenance, and academic use.
The university adopted an AI-powered document processing solution built on iCaptur technology to modernize its archive. Advanced OCR and image processing were applied to handle degraded scans and handwritten content, restoring clarity to aged technical diagrams. Automated feature extraction captured specifications, component details, and metadata directly from typed, handwritten, and hand-drawn documents. Intelligent indexing created a structured digital repository, enabling quick retrieval across decades of records. Noise reduction and image enhancement improved readability, while large files were optimized for efficient storage and access. Together, these capabilities transformed the archive into a searchable and well-organized knowledge base.
The project successfully digitized and preserved critical historical engineering knowledge, making decades of documentation fully accessible to staff and researchers. Search and retrieval times were reduced dramatically, saving significant effort previously lost to manual reviews. High-accuracy indexing enabled quick access to technical data, supporting maintenance, research, and educational projects. The initiative improved operational efficiency, strengthened document preservation, and ensured seamless data migration of the entire archive despite the poor quality of source material. The University now benefits from a reliable, accessible, and future-proof digital repository for its power plant documentation.
